Device-native foundation models.

Advanced intelligence for processors outside of data centers. Built for the latency, privacy, and hardware constraints of the physical world.

Runtimes

llama.cpp, MLX, ONNX, CoreML, SGLang, vLLM

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Announcement
Engineering

Setting the Frontier of Aging Biology with Liquid Foundation Models

  • “Liquid AI's models are the best for the scaled production deployment - pareto-optimal, beating much larger rivals. That's why we use them at Shopify.”
    Mikhail ParakhinCTO, Shopify
  • “Because of their very small footprint relative to their power, Liquid AI’s models are [...] an ideal candidate to run for yourself. If you are an enterprise, a small company, or a physical AI company, you can run these models on your own servers and still get tremendous power.”
    Ion StoicaCo-Founder of Arena, Databricks, and Anyscale
  • “You’re creating [a new] class of where AI can be deployed and how it can be deployed. And I particularly love it because you’re rethinking how to optimize [and] how AMD as a chip developer can work with you as a practitioner of the state-of-the-art AI to create this whole new class of AI computation.”
    Mark PapermasterCTO, AMD
  • “By advancing on-device speech, language understanding and reasoning with Liquid AI, we’re laying the foundation for the next generation of intuitive and multimodal in-car experiences.”
    Jörg BurzerCTO, Mercedes-Benz

Achieve peak performance by fine-tuning LFMs for your use case.

The power of model customization directly in your hands.

LFMs are designed for rapid customization to achieve peak performance for specified use cases at a footprint small enough to run locally on your chosen hardware. Our full-stack solution includes architecture, optimization, and deployment engines to accelerate the path from prototype to product.